ArticleFrontiers in plant science2022
Estimation of wheat tiller density using remote sensing data and machine learning methods.
Article in Frontiers in plant science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed.
- Large-Scale Mapping of Maize Plant Density Using Multi-Temporal Optical and Radar Data: Models, Potential and Application Strategy.Plants (Basel, Switzerland) · 2024Article
- Accurate estimation of fractional vegetation cover for winter wheat by integrated unmanned aerial systems and satellite images.Frontiers in plant science · 2023Article
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Authors and funding
15 authors.
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Abstract
The tiller density is a key agronomic trait of winter wheat that is essential to field management and yield estimation. The traditional method of obtaining the wheat tiller density is based on manual counting, which is inefficient and error prone. In this study, we established machine learning models to estimate the wheat tiller density in the field using hyperspectral and multispectral remote sensing data. The results showed that the vegetation indices related to vegetation cover and leaf area index are more suitable for tiller density estimation. The optimal mean relative error for hyperspectral data was 5.46%, indicating that the results were more accurate than those for multispectral data, which had a mean relative error of 7.71%. The gradient boosted regression tree (GBRT) and random forest (RF) methods gave the best estimation accuracy when the number of samples was less than around 140 and greater than around 140, respectively. The results of this study support the extension of the tested methods to the large-scale monitoring of tiller density based on remote sensing data.
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